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Mumford, Christine Lesley (2004)
Publisher: Springer
Languages: English
Types: Unknown
Subjects: QA75, QA76
This paper explores some simple evolutionary strategies for an elitist, steady-state Pareto-based multi-objective evolutionary algorithm. The experimental framework is based on the SEAMO algorithm which differs from other approaches in its reliance on simple population replacement strategies, rather than sophisticated selection mechanisms. The paper demonstrates that excellent results can be obtained without the need for dominance rankings or global fitness calculations. Furthermore, the experimental results clearly indicate which of the population replacement techniques are the most effective, and these are then combined to produce an improved version of the SEAMO algorithm. Further experiments indicate the approach is competitive with other state-of-the-art multi-objective evolutionary algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Corne D W, Knowles J D, and Oates M J: The Pareto envelope-based selection algorithm for multiobjective optimization. Parallel Problem Solving from Nature { PPSN VI, Lecture Notes in Computer Science 1917 (2000) 839{848, Springer.
    • 2. Deb K, Agrawal S, Pratap A, and Meyarivan T: A fast elitist non-dominated sorting genetic algorithm for mult-objective optimization: NSGA-II, Parallel Problem Solving from Nature { PPSN VI, Lecture Notes in Computer Science 1917 (2000) 849{858, Springer.
    • 3. Goldberg D E: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley (1989).
    • 4. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996).
    • 5. Mumford C L (Valenzuela): Comparing representations and recombination operators for the multi-objective 0/1 knapsack problem, Congress on Evolutionary Computation (CEC) Canberra Australia (2003) 854{861.
    • 6. Mumford C L (Valenzuela): A hierarchical approach to multi-objective optimization, Congress on Evolutionary Computation (CEC) Portland, Oregon (2004) (to appear).
    • 7. Mumford-Valenzuela C L: A Simple Approach to Evolutionary Multi-Objective Optimization, In Evolutionary Computation Based Multi-Criteria Optimization: Theoretical Advances and Applications, edited by Ajith Abraham, Lakhmi Jain and Robert Goldberg. Springer Verlag (2004) London.
    • 8. Oliver I M, Smith D J, and Holland J R C: A study of permutation crossover operators on the traveling salesman problem, Genetic Algorithms and their Applications:Proceedings of the Second International Conference on Genetic Algorithms (1987) 224{230.
    • 9. Valenzuela C L: A simple evolutionary algorithm for multi-objective optimization (SEAMO), Congress on Evolutionary Computation (CEC), Honolulu, Hawaii (2002) 717{722.
    • 10. Zitzler E and Thiele L: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach, IEEE Transactions on Evolutionary Computation, 3(4) (1999) 257{271.
    • 11. Zitzler E, Laumanns M, and Thiele L: SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-Report 103, Department of Electrical Engineering, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, fzitzler, laumanns, .(2001) (Data and results downloaded from: http://www.tik.ee.ethz.ch/zitzler/testdata.html)
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